Obstacle Classification Method Based On 2D LIDAR Database

Authors: Moohyun Lee, Soojung Hur, Yongwan Park

Abstract:

We propose obstacle classification method based on 2D LIDAR Database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width and intensity data; the first classification was processed by the width data; the second classification was processed by the intensity data; classification was processed by comparing to database; result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.

Keywords: Obstacle, Classification, LIDAR, Segmentation, Width, Intensity, Database.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1094367

References:

[1] Z. Yankun, "A Single Camera Based Rear Obstacle Detection System,” Intelligent Vehicle Symposium, Baden-Baden, 2011, pp. 485-490.
[2] J. Wenger, "Automotive Radar-Status and Perspectives,” Compound Semiconductor Integrated Circuit Symposium, 2005.
[3] A. Kirchner, T. Heinrich, "Model Based Detection of Road Boundaries with a Laser Scanner,” IEEE International Conference on Intelligent Vehicles, Stuttgart, 1998, pp. 93-98.
[4] K. Chu, J. Han, M. Lee, D. Kim, K. Jo, D. Oh, E. Yoon, M. Gwak, K. Han, D. Lee, B. Choe, Y. Kim, K. Lee, K. Huh, M. Sunwoo, "Development of an Autonomous Vehicle: A1,” Transactions of KSAE, Vol.19, No.4, pp. 146-154, 2011.
[5] D. M. Cole, P. M. Newman, "Using Laser Range Data for 3D SLAM in Outdoor Environment,” Robotics and Automation, Orlando, pp. 1556-1563, 2006.
[6] S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, K. Lau, C. Oakley, M. Palatucci, V. Pratt, P. Stang, S. Strohband, C. Dupont, L. E. Jendrossek, C. Koelen, C. Markey, C. Rummel, J. vanNiekerk, E. Jensen, P. Alessandrini, G. Bradski, B. Davies, S. Ettinger, A. Kaehler, A>Nefian, P. Mahoney, "Stanley: The Robot that Won the DARPA Grand Challenge,” Journal of Field Robot, Vol. 23, No. 9, pp. 661-692, 2006.
[7] R. Halterman, M. Bruch, "Velodyne HDL-64E LIDAR for Unmanned Surface Vehicle Obstacle Detection,” SPIE Defense, Security and Sensing, pp. 76920D-76920D-8, 2010.
[8] L. Iocchi, S. Pellegrini, "Building 3D Map with Semantic Elements Integrating 2D Laser, Stereo Vision and IMU on a Mobile Robot,” 2nd International Society for Photogrammetry and Remote Sensing International Workshop 3D-ARCH, 2007.
[9] H. Moon, J. Kim, "Obstacle Detecting System for Unmanned Ground Vehicle using Laser Scanner and Vision,” 2007 International Conference on Control, Automation and System, 2007.
[10] D. Habermann, A. Hata, D. Wolf, F. Osorio, "Artificial Neural Nets Object Recognition for 3D Point Cloud,” Intelligent System, pp. 101-106, 2013.
[11] J. Hancock, "Laser Intensity-based Obstacle Detection and Tracking,” Carnegi Mellon University, 1999.
[12] A. Carballo, A. Ohya, S. Yuta, "People Detection using Range and Intensity Data from Multi-Layered Laser Range Finders,” Intelligent Robots and System, Taipei, 2010, pp. 5849-5854.
[13] O. Hadjiliadis, I. Stamos, "Sequential Classification in Point Cloud of Urban Scenes,” Proceeding of 3DPVT, 2010.